•

•

•

•

•

Act:

Score,

Visualize

Deploy Apps,

Services &

Visualizaitons

Measure

Preparation Modeling

Feature &

Algorithm

Selection

Model

Testing &

Validation

Operationalization

Models

Visualizations

Ingest

Profile

Explore

Visualize

Transform

Cleanse

Denormalize

Advanced Analytics Portfolio

5

SSRS

SharePoint BI

Excel BI

Datazen

SQL Server

SQL Server

Integration Services SQL Server Analysis Services

Hadoop

(on-premise / Azure VM)

SQL Server

MDS, DQS

Applications

HIVE

SQOOPPIG

OOZIE

SQL

HBase

Hadoop/OSOn-premise

✓ Comprehensive Connected Choice✓ ✓

Storm

R Server

Intelligence Cognitive Services, Bot Framework. Cortana

Dashboards and Visualizations Power BI SSRS, DataZen, R R visualizations

Machine learning and Analytics Machine learning, Stream Analytics, HDInsight SSAS, SQL Server R Services Microsoft R

Big Data Store Data Lake, SQL, DW OLTP, DW, Hadoop, EDSs Hadoop, Teradata, Linux, Windows

Information Management Data Factory, Data Catalog, Event Hubs SSIS, DQS, MDS Spark SQL/ETL

Cortana Intelligence Suite Microsoft RSQL Server 2016

In Database analytics R-based analyticsCloud analytics

Transform data into intelligent action

Intelligence

Cortana

Cognitive

Services

Bot

Framework

Preconfigured Solutions

Dashboards and Visualizations

Machine Learning and Analytics

Big Data Store

Information Management

Easily build, deploy, and share predictive analytics solutions

• Simple, scalable, cutting edge. A fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions.

• Deploy in minutes. Azure Machine Learning means business. You can deploy your model into production as a web service that can be called from any device, anywhere and that can use any data source.

• Publish, share, monetize. Share your solution with the world in the Gallery or on the Azure Marketplace.

Machine Learning

and Analytics

HDInsight

(Hadoop and

Spark)

Stream

Analytics

Data Lake

Analytics

Machine

Learning

Demo

??

Lack of Commercial

Support

Single-threaded /

Not parallelized

Complex DeploymentProcessesProduction

Limited Data Scale

inMemory

Challenges posed by open source R

Microsoft R Products

• Free and open source R distribution

• Enhanced and distributed by Microsoft

Microsoft R Open / Client

• Built in Advanced Analytics and Stand Alone Server Capability

• Leverages the Benefits of SQL 2016 Enterprise Edition

SQL Server R Services

• Microsoft R Server for Redhat Linux

• Microsoft R Server for SUSE Linux

• Microsoft R Server for Teradata DB

• Microsoft R Server for Hadoop on Redhat

Microsoft R Server

Microsoft R products

MRS extends open-source R to allow:

• Multi-threading• Matrix operations, linear algebra, and many other math operations run on all

available cores

• Parallel processing• ScaleR functions utilize all available resources, local or distributed

• On-disk data storage• RAM limitations lifted

Microsoft R Server

R Open Microsoft R Server

DeployR

ConnectR• High-speed & direct

connectors

Available for:• High-performance XDF

• SAS, SPSS, delimited & fixed format text data files

• Hadoop HDFS (text & XDF)

• Teradata Database & Aster

• EDWs and ADWs

• ODBCScaleR• Ready-to-Use high-performance

big data big analytics

• Fully-parallelized analytics

• Data prep & data distillation

• Descriptive statistics & statistical tests

• Range of predictive functions

• User tools for distributing customized R algorithms across nodes

• Wide data sets supported – thousands of variables

DistributedR• Distributed computing framework

• Delivers cross-platform portability

R+CRAN• Open source R interpreter

• R 3.1.2

• Freely-available huge range of R algorithms

• Algorithms callable by RevoR

• Embeddable in R scripts

• 100% Compatible with existing R scripts, functions and packages

RevoR• Performance enhanced R

interpreter

• Based on open source R

• Adds high-performance math library to speed up linear algebra functions

MRS Components

https://info.microsoft.com/rs/157-GQE-382/images/EN-WBNR-Slidedeck-Using-Microsoft-R-Server-for-Scalability.pdf

Platforms

Demo

Flexibility & Agility

Cost Effective

Performance & Scale

Manage and

Monitoring

Microsoft R Open

Microsoft R Server

R IDE

Data Scientist

WorkstationSQL Server 2016Script

Results

Execution

1

2

3

sqlCompute <- RxInSqlServer()

rxSetComputeContext(sqlCompute)

linModObj <- rxLinMod()

Advanced Analytics

Extensions

Model Development (R Users)

Working from R IDE on a local workstation, execute an R script that runs in-database on remote SQL

server, and get the results back.

Microsoft R Open

Microsoft R Server

Application

SQL Server 2016

2

exec sp_execute_external_script

@language = ‘R’, @script = -- R code --

Advanced Analytics

Extensions

Microsoft R Open

Mcirosoft R Server

Call System Stored Procedure1

Results: scores, plots3

The stored procedure

contains R code and

executes in-database

Model Operationalization with SQL R Services

Call a T-SQL System Stored Procedure to generate features and train (or retrain) the model

Call a T-SQL System Stored Procedure from my application and have it trigger R script execution in-

database to predict on new dataset. Results are then returned to my application (predictions, plots).

R Code->T-SQL Stored Proc

R script usage from SQL Server

Original R script:

IrisPredict <- function(data, model){library(e1071)predicted_species <- predict(model, data)return(predicted_species)

}

library(RODBC)conn <- odbcConnect("MySqlAzure", uid = myUser, pwd = myPassword);Iris_data <-sqlFetch(conn, "Iris_Data");Iris_model <-sqlQuery(conn, "select model from my_iris_model");IrisPredict (Iris_data, model);

Calling R script from SQL Server:

/* Input table schema */create table Iris_Data (name varchar(100), length int, width int);/* Model table schema */create table my_iris_model (model varbinary(max));

declare @iris_model varbinary(max) = (select model from my_iris_model);exec sp_execute_external_script@language = 'R'

, @script = 'IrisPredict <- function(data, model){library(e1071)predicted_species <- predict(model, data)return(predicted_species)

}IrisPredict(input_data_1, model);', @parallel = default, @input_data_1 = N'select * from Iris_Data', @params = N'@model varbinary(max)', @model = @iris_modelwith result sets ((name varchar(100), length int, width int, species varchar(30)));

Values highlighted in yellow are SQL queries embedded in the original R script

Values highlighted in aqua are R variables that bind to SQL variables by name

Advanced analytics

CapabilityExtensible in-database analytics, integrated with R,

exposed through T-SQL

Centralized enterprise library for analytic models

Benefits

SQL Server

Analytical engines

Integrate with R

Become fully extensible

Data management layer

Relational data

Use T-SQL interface

Stream data in-memory

Analytics library

Share and collaborate

Manage and deploy

R +

Data Scientists

Business

Analysts

Publish algorithms, interact

directly with data

Analyze through T-SQL,

tools, and vetted algorithms

DBAsManage storage and

analytics together

R integration and advanced analytics

Advanced analytics

Summary: Advanced Analytics Products

Intelligence Cognitive Services, Bot Framework. Cortana

Dashboards and Visualizations Power BI SSRS, DataZen, R R visualizations

Machine learning and Analytics Machine learning, Stream Analytics, HDInsight SSAS, SQL Server R Services Microsoft R

Big Data Store Data Lake, SQL, DW OLTP, DW, Hadoop, EDSs Hadoop, Teradata, Linux, Windows

Information Management Data Factory, Data Catalog, Event Hubs SSIS, DQS, MDS Spark SQL/ETL

Cortana Intelligence Suite Microsoft RSQL Server 2016

In Database analytics R-based analyticsCloud analytics

Microsoft Cognitive ServicesGive your apps a human side

Microsoft Cognitive ServicesGive your apps a human side

Cognitive Services API Collection

Filtering the signal from the noise

Rolls-Royce

“Our goal is not data for the sake of data, but to embrace the cloud and analytical technologies to deliver more expert insights to the right stakeholders at the right time.“

Nick Farrant

Senior Vice President

ROLLS-ROYCE

ObjectivesConnect Rolls-Royce jet

engine data to Microsoft's

intelligent cloud for insights

to improve aircraft

performance, safety and

maintenance.

TacticsUse Cortana Intelligence

to scale quickly and

efficiently, aggregate data

across customer fleets

and process data

in real time.

Results• More efficient flight and

maintenance plans

• Targeted and actionable fuel

efficiency insights

• Quickly-generated reports and

dashboards that tell compelling

stories and deliver high-quality

insights

Microsoft and Rolls-Royce collaborate to offer advanced operational intelligence to airlines

“Using our energy management

platform based on Azure, they can

cut those peak hours, avoid the

upgrade, and save a lot of money.”

- Knut Johansen, CEO

Solution

Rolls Royce uses Cortana

Intelligence to connect jet

engine data to Microsoft's

Cloud to improve aircraft

performance, safety and

maintenance

Results

• Drive 1-2% increase in fuel

efficiency, saving $Ms/year.

• Use savings from actionable

insight to offer additional

services to customers.

• Do failure prediction for next

flights and fleet monitoring

using anomaly detection.

DEMO